rna sequencing rna seq  (Human Protein Atlas)

Journal: Regenerative Therapy

Article Title: Sex as a biological variable in human dental pulp stem cells: An exploratory epigenomic and transcriptomic comparison

doi: 10.1016/j.reth.2026.101117

Figure Lengend Snippet: Bulk RNA-seq of human DPSCs from male and female donors. (A) Principal component analysis of rlog-normalized counts for the top 500 most variable genes with concentration eclipses showing within-group dispersion. Each point represents one donor. (B) Pairwise sample–sample correlation matrix between male (M) and female (F) DPSCs. Colors show Pearson correlation coefficients. (C) Unsupervised hierarchical clustering heatmap of all expressed genes using Euclidean distance and complete linkage. (D) Summary of differentially expressed genes (DEGs) in male DPSCs compared to female DPSCs. Outer circles show totals at adjusted p value < 0.05, and inner circles show the subset with |log 2 FC| > 1. (E) MA plot showing sex-biased gene expression in DPSCs. Statistically significant DEGs (adjusted p < 0.05) are marked in green, blue, and red for autosomal, X-linked, and Y-linked genes, respectively. (F) Volcano plot of RNA-seq data with represented genes labelled.

Article Snippet: Bulk RNA sequencing (RNA-seq) was performed by Novogene Ltd. (Cambridge, UK).

Techniques: RNA Sequencing, Concentration Assay, Dispersion, Gene Expression

Journal: Frontiers in Immunology

Article Title: CD32b defines distinct dendritic cell lineages generated from the culture of bone marrow with GM-CSF

doi: 10.3389/fimmu.2026.1703978

Figure Lengend Snippet: CD32b distinguishes transcriptionally and functionally distinct GM-DC subsets derived from MDPs and GMPs. (A) Principal component analysis (PCA) of bulk RNA-seq data from GM-DCs sorted on day 7 and day 21. DCs were generated from cultures of whole bone marrow (BM), sorted MDPs, or sorted GMPs with GM-CSF. (B) Heatmap showing the expression of genes upregulated and downregulated in MDP GM-DCs at day 7, identified through Venn diagram analysis and mapped onto BM GM-DCs at both time points. (C) Volcano plot of differentially expressed genes (DEGs) between MDP GM-DCs (day 7) and GMP GM-DCs (day 21). The thresholds for significance (dotted lines) are set at a log 2 (fold change) ≥ 3 or raw p -value ≤ 10 -16 . (D) Flow cytometric validation of selected surface marker candidates identified in the volcano plot (C) . Normalized Mean fluorescence intensity (MFI) of four representative markers is shown for each DC subset. Normalized MFI was calculated by subtracting the isotype control mean fluorescence from the sample mean fluorescence, and then dividing the result by the isotype control mean. Representative histograms are shown below the bar graphs, with the isotype control overlaid in gray. Data are representative of two independent experiments.

Article Snippet: RNA sequencing (RNA-seq) procedure and analysis were performed by Macrogen (Seoul, Korea).

Techniques: Derivative Assay, RNA Sequencing, Generated, Expressing, Biomarker Discovery, Marker, Fluorescence, Control

Journal: Frontiers in Immunology

Article Title: Identification of mitochondria-related biomarkers in liver fibrosis via interpretable machine learning and WGCNA: transcriptomic analysis and In Vivo validation

doi: 10.3389/fimmu.2026.1705706

Figure Lengend Snippet: Single-cell transcriptomic analysis of liver fibrosis. (A) Quality control metrics before cell filtering, including the distribution of gene counts (nFeature_RNA), UMI counts (nCount_RNA), and the percentages of mitochondrial and hemoglobin genes across samples. (B) Cell clustering of liver fibrosis samples. (C) Cell-type annotation of single-cell RNA-seq data. (D) Cell cycle analysis of single-cell transcriptomic data. (E) Proportional changes of different cell types between normal and fibrotic groups. (F) Expression distribution of Acot9, Aldh1b1, and Pck2 across different cell types.

Article Snippet: Single-cell RNA sequencing (scRNA-seq) datasets were obtained from GSE145086 and GSE233084 , both generated using the 10X Genomics platform ( , ).

Techniques: Single Cell, Control, RNA Sequencing, Cell Cycle Assay, Expressing